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Identifying the contributions of multiple driving forces to PM10–2.5 pollution in urban areas in China
Science of the Total Environment 663 (2019) 361–368
Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Identifying the contributions of multiple driving forces to PM10–2.5 pollution in urban areas in China Shuang Zhao a, Shiliang Liu a,⁎, Xiaoyun Hou a, Robert Beazley b, Yongxiu Sun a a b
School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, China Department of Natural Resources, College of Agriculture and Life Sciences, Fernow Hall 302, Cornell University, Ithaca, NY 14853, USA
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• PM10–2.5 poses a serious threat to human beings from a public health point of view. • Structural equation model is used to determine the driving mechanisms. • Urban PM10–2.5 pollution was concentrated in central regions of China. • Industrial scale contributed most to PM10–2.5 pollution in urban areas. • Eleven recommendations to control PM10–2.5 pollution are proposed.
a r t i c l e
i n f o
Article history: Received 11 October 2018 Received in revised form 23 December 2018 Accepted 18 January 2019 Available online 23 January 2019 Editor: Pavlos Kassomenos Keywords: PM10–2.5 pollution Mechanism analysis Anthropogenic source Driving force National scale
a b s t r a c t Economic development and urban expansion have accelerated particulate matter pollution in urban areas in China. Particulate matter-driven haze poses a serious threat to human beings from a public health point of view. Substantial evidences had linked adverse health effects with exposures to PM2.5, but recent research indicated that PM10–2.5 also had great risk. However, the relative contributions of driving forces to PM10–2.5 pollution are not well understood in the urban areas in China, and no targeted policies have been regulated to control the pollution. In this study, we quantified the contributions of potential driving factor across China with the structural equation model (SEM). Our results showed that in 2015 and 2016, the annual average concentrations of PM10–2.5 in the 290 prefecture-level cities with a mean value of 36 and 35 μg/m3, respectively. Industrial scale contributed more to PM10–2.5 pollution than city size and residents' activities in urban areas based on SEM results. Driving forces included in our model could explain 42% of variations in PM10–2.5 pollution, which indicated that there existed influences from other anthropogenic sources and natural sources. Eleven targeted recommendations were then proposed to control PM10–2.5 pollution based on our mechanism analysis. Findings from our study are beneficial to control PM10–2.5 pollution on a national scale, and also can provide a theoretical basis for the formulation of PM10–2.5 pollution control policy in China. © 2019 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding author. E-mail address: [email protected] (S. Liu).
https://doi.org/10.1016/j.scitotenv.2019.01.256 0048-9697/© 2019 Elsevier B.V. All rights reserved.
Traffic, industrial activities, biomass burning, and rapid population growth in global urban areas all accelerate particulate matter pollution, which rapidly reduce air quality of the cities (Chirizzi et al., 2017;
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Karagulian et al., 2015). Particulate matter-driven haze became a common air pollution phenomenon in many urban areas of China (Liu et al., 2018a). The concentration of particulate matter far exceeds the national air quality standard of China (Wang et al., 2014). Numerous studies verified the adverse effects of short-term exposures to fine particulate matter (PM2.5; ≤2.5 μm in aerodynamic diameter) on human health (Pascal et al., 2014), while the adverse effects of coarse particulate matter (PM10–2.5; 2.5–10 μm in aerodynamic diameter) had been often ignored (Kassomenos et al., 2012). Recent research indicated that long-term or short-term exposures to PM10–2.5 was seriously hazardous to human life span (Ho et al., 2018; Pascal et al., 2014; Wu et al., 2018), artery (Adar et al., 2015; Zhang et al., 2014), pregnant women (Enders et al., 2019), infant survival (Son et al., 2011), infant birth weight (Ebisu et al., 2016), elderly (Han et al., 2017; Hassanvand et al., 2017), lower socio economic status (Ouidir et al., 2017), blood pressure (Honda et al., 2017), cerebrovascular (Wang et al., 2018) and reproductive health (Zhou et al., 2018). Although some aforementioned studies confirmed the harm of PM10–2.5, China had not set health-based standards for PM10–2.5. In 2016, the Chinese government gave up the ‘one-child’ policy, which had been implemented for N30 years. Since then, a large number of pregnant women and infants increased. Since PM10–2.5 is equally harmful to living organisms as PM2.5, we use the quality standard of PM2.5 to evaluate the pollution level of PM10–2.5 in our research. The annual average concentrations of PM10–2.5 in cities reached 36 μg/m3 and 35 μg/m3 in 2015 and 2016, respectively. These values are all overpassed the national air quality standard of China for PM2.5 (annual average concentrations b35 μg/m3). PM10–2.5 pollution has become an urgent air problem in China (Rönkkö et al., 2018). PM10–2.5 pollution is produced by a range of natural (such as windblown dust) and anthropogenic processes (such as vehicle emission, industrial and construction) in urban areas (Kassomenos et al., 2012). Compared with PM2.5, less is known about the driving forces of PM10–2.5 (Clements et al., 2016). Anthropogenic processes such as vehicle emissions, industrial and construction are considered to be the main source of PM10–2.5 pollution in China (Kassomenos et al., 2012; Lee et al., 2017). Some researchers had pointed out that industrial processing was the major source (Bano et al., 2018; Clements et al., 2014; Sawvel et al., 2015). Secondary inorganic aerosol emissions containing PM10–2.5 which were largely ascribed to vehicles (Bigi and Ghermandi, 2016; Clements et al., 2014) and ship (Bencs et al., 2017). Dust produced by construction, quarries, unpaved roads, and soil erosion made contributions to PM10–2.5 pollution in the urban areas (Li et al., 2013; Raysoni et al., 2016). Domestic cooking also promoted PM10–2.5 pollution (Bano et al., 2018). To determine the contributions of different factors, Landis et al. (2017) quantitatively analyzed PM10–2.5 sources in Athabasca Oil Sand Region using positive matrix factorization method. The results showed that fugitive haul road dust, fugitive oil sand, a mixed source fugitive dust, biomass combustion, mobile source, and a local copper factor were the top six main sources. Shahid et al. (2016) had similar conclusions that calcareous dust, siliceous dust, combustion particles and secondary organics had an 84% contribution to PM10–2.5 at an urban site in Pakistan. However, although higher concentrations of PM10–2.5 pollution were often found in urban areas in China, published literatures on generation mechanism were still limited, making region-specific mechanism difficult to interpret. The previous research lacked quantitative analysis about the multiple socioeconomic driving forces for PM10–2.5 pollution. The structural equation model (SEM) is a useful statistical approach to determine the theoretical causal relationships between driving forces and PM10–2.5 pollution (Beaumelle et al., 2016). In this study, we selected 290 prefecture-level cities with complete data in 2016 (including PM10–2.5 pollutant monitoring data and socioeconomic data) to examine the influence mechanisms of multiple forces driving urban PM10–2.5 pollution at national scale by constructing a reasonable SEM. The objectives of this research were to 1) quantify the
relative contribution of individual forces driving urban PM10–2.5 pollution; and 2) make policy recommendations for controlling PM10–2.5 pollution. The results of this study will provide theoretical advices for the formulation of urban PM10–2.5 control policies in the future. 2. Materials and methods 2.1. PM10–2.5 data collection After the optimization and adjustment of the National Air Monitoring Network, the monitoring sites expanded to the present 338 prefecture-level cities across China, and the number of ambient air quality monitoring points had been adjusted to 1497. All the monitoring data is uploaded to the National Urban Air Quality Real-time Publishing Platform of the China Environmental Monitoring Station (http://106.37. 208.233:20035/). The PM10–2.5 pollution data (2015 and 2016) we used in this study were downloaded from this platform (Appendix S1). After reviewing the integrity of the socioeconomic data, we finally selected 290 prefecture-level cities in 2016 across the country in which to analyze the impact of multiple socioeconomic factors on PM10–2.5 pollution (Fig. 1). 2.2. Socioeconomic data collection Two criteria were used to screen out the appropriate socioeconomic data. Firstly, the socioeconomic factors must have a direct or indirect impact on PM10–2.5 pollution, and these effects must be also documented by corresponding references. Secondly, there must be available data sources for the selected socioeconomic factors in all selected prefecture-level cities. Therefore, ten socioeconomic driving forces were selected according to the above criteria as follows: resident population, urban built-up area, secondary industry GDP, tertiary industry GDP, power generation, electricity consumption for industrial, household electricity consumption, total retail sales of consumer goods, civilian vehicles, and urban heated area (Table 1). Data for socioeconomic driving forces were collected from the China urban construction statistics yearbooks, China city statistical yearbook, corresponding literatures (Jiang et al., 2018), and official website data (Appendix S2). 2.3. Data analysis We established an initial SEM used the underlying relationships between different factors (Fig. 2). In the initial SEM, three latent variables were established, namely city size (determined by the observed variables of resident population and urban built-up area), industrial scale (determined by the observed variables of secondary industry GDP, tertiary industry GDP, power generation, and electricity consumption for industry), and residents' activities (determined by the observed variables of household electricity consumption, total retail sales of consumer goods, civilian vehicle, and urban heated area). Among these variables, industrial scale and residents' activities showed a direct driving forces on PM10–2.5 pollution, while city size was indirect. Akaike information criterion (AIC) and comparative fit index (CFI) were used to test the model. The optimal model should have the lowest AIC value and CFI value N0.90 (Bentler and Appelbaum, 1990). SEM analysis were performed in Amos 21.0 and R 3.4.2 (R Development Core Team, 2017). 3. Results 3.1. PM10–2.5 pollution level in urban China In 2015, the annual average concentrations of PM10–2.5 in the studied prefecture-level cities varied from 12 μg/m3 (Huangshan City) to 83 μg/ m3 (Jiayuguan City), with a mean value of 36 μg/m3 (Fig. 3, Appendix S1). The 90 percentile concentrations of PM10–2.5 varied from 20 μg/m3
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Fig. 1. The location of the selected prefecture-level cities in different provinces.
(Huangshan City) to 145 μg/m3 (Jiuquan City), with a mean value of 61 μg/m3. The number of heavy PM10–2.5 pollution days varied from 0 to 165 days (Hengshui City), with a mean value of 28 days. There were only 62 prefecture-level cities where heavy PM10–2.5 pollution had not occurred. In 2016, the annual average concentrations of PM10–2.5 in the studied prefecture-level cities varied from 10 μg/m3 (Heyuan City) to 83 μg/m3
(Jiuquan City), with a mean value of 35 μg/m3. The 90 percentile concentrations of PM10–2.5 varied from 18 μg/m3 (Nanping City) to 144 μg/m3 (Jiuquan City), with a mean value of 58 μg/m3. The number of heavy PM10–2.5 pollution days varied from 0 to 131 days (Jiuquan City), with a mean value of 26 days. There were 55 prefecture-level cities where heavy PM10–2.5 pollution had not occurred. It was evident that PM10–2.5 pollution in the two years were serious but varied insignificantly.
Table 1 Selected socioeconomic driving forces and their corresponding references.
3.2. The results of SEM
Driving forces
Reference sources
Industrial scale Secondary industry GDP Tertiary industry GDP Power generation Electricity consumption for industrial
Clements et al. (2014); Sawvel et al. (2015) Li et al. (2013); Raysoni et al. (2016) Landis et al. (2017); Shahid et al. (2016) Shahid et al. (2016)
City size Resident population Urban built-up area Residents' activities Household electricity consumption Total retail sales of consumer goods Civilian vehicles Urban heated area
Lamsal et al. (2013); Tian et al. (2013) Lyu et al. (2016); Raysoni et al. (2016)
Bano et al. (2018); Lamsal et al. (2013); You et al. (2017) Lyu et al. (2016) Bigi and Ghermandi (2016); Clements et al. (2014) Schiavon et al. (2015)
The influence of driving forces on PM10–2.5 pollution in SEM was shown in Fig. 4. The model had the lowest AIC value, and the CFI was 0.97 after the tertiary industry GDP factor was removed (Appendix S3). Industrial scale contributed to PM10–2.5 pollution more than other latent variables, and secondary industry GDP were found to have a higher impact on PM10–2.5 pollution than other observed variables, while urban heated area had the lowest (Table 2). Overall, the variables included in our model could explain 42% of variation in PM10–2.5 pollution. 4. Discussion 4.1. Spatial distribution of PM10–2.5 pollution in urban areas in China From the perspective of the whole China, PM10–2.5 pollution is concentrated in central regions, especially in Henan Province, Hebei Province, Shanxi Province, Shandong Province and Beijing city (Figs. 1, 2). These five areas had 330 million permanent residents in 2016, 4
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δ31
δ32
δ41
δ42
Urban builtup area
Resident population
Total retail sales of consumer goods
Civilian vehicles
δ43
δ44
Household electricity consumption
Urban heated area
City Size
Residents’ Activities
Industrial Scale
PM10-2.5 Pollution
Tertiary industry GDP
Secondary industry GDP
Power generation
Electricity consumption for industrial
Annual mean concentration of PM10-2.5
Heavy PM10-2.5 pollution days
90 percentile concentrations of PM10-2.5
δ21
δ22
δ23
δ24
δ11
δ12
δ13
Fig. 2. The initial frame for SEM. Blue represent driving forces that directly affect PM10–2.5 pollution, while green represent driving forces that indirect affect PM10–2.5 pollution. δ represent errors of the observed variables. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
national-level urban agglomerations, and a large number of industrial bases such as steel and coal mines, which all accelerated the production of PM10–2.5 pollution according to relevant literatures and our research results. The annual average concentrations of PM10–2.5 in cities of central China were mostly in the scope of the secondary standard (35–75 μg/ m3). The data used in this research were the annual average concentrations of PM10–2.5, which in some urban areas might mask the differences of PM10–2.5 concentrations between different months. For example, in Handan city of Hebei Province, the concentration of PM10–2.5 was 37 μg/m3 in the July, while it became 106 μg/m3 in the April and appeared to be serious PM10–2.5 pollution. The annual average PM10–2.5 concentrations in most southern cities were within the range of the first class standard (b35 μg/m3).
4.3. City size and PM10–2.5 pollution According to our research results, city size is an important indirect driver of PM10–2.5 pollution in China's urban areas, its contribution to PM10–2.5 pollution is through influencing residents' activities (Table 2). The increase of city size means more civilian vehicles, more construction activities and more electricity consumption, all of which contribute to PM10–2.5 pollution (Zhou et al., 2016). Lou et al. (2016) took urban areas as a ‘source’ landscape of particulate matter, and our results confirmed this statement. In addition, as the close connections of atmospheric circulation and regional atmospheric pollution occurs in northern China (Tao et al., 2014), PM10–2.5 can be long-range atmospheric transported (Kassomenos et al., 2012), joint prevention and control of PM10–2.5 pollution should be carried out among urban agglomerations.
4.2. Industrial scale and PM10–2.5 pollution In northern China, developed industrial activities such as steel and cement production remain the most important sources of urban particulate matter (Sawvel et al., 2015; Sanchez-Soberon et al., 2016). Significantly higher rates of lung cancer, respiratory and cardiovascular pathologies occurred due to industrial emissions of metal-rich particulate matter in the cities (Ruiz-Rudolph et al., 2016; Uzu et al., 2011). Reasonable adjustment of the proportions of secondary industry and tertiary industry, development of high-tech industry and transformation of the old industrial production system have become the key to reducing the level of particulate pollution in the cities. Thermal power generation produces large amounts of particle pollutants (RuizRudolph et al., 2016). The proportion of nuclear and hydroelectric power will increase due to less pollutant emissions. At present, the Chinese government has promulgated a series of policies to reduce the contribution of industrial activities to urban particulate pollution. For example, in order to reduce the concentration of particulate matter in the winter of the Beijing-Tianjin-Hebei region, ‘Action Plan for Comprehensive Treatment of Air Pollution in Autumn and Winter in Jing-Jin-Ji and Surrounding Areas’ have been implemented in heavy polluting industries such as steel, manufacturing and building materials in Hebei Province. In the foreseeable future, the contribution rate of industrial activities to urban air pollution will be significantly reduced due to the implementation of government targeted measures.
4.4. Residents' activities and PM10–2.5 pollution Residents' activities are another important direct driving forces of urban PM10–2.5 pollution. Civilian vehicles, household electricity consumption and total retail sales of consumer goods have similar contribution rates (Table 2). Civilian vehicles are considered to be the source of various air pollutants in the city (Zhao et al., 2018a), and residents near traffic network are affected by traffic-related PM10–2.5 pollution which can bring health risks (Oakes et al., 2016). With the increase of income, more vehicles are purchased and used by urban residents, and the wide use of low-quality gasoline have further reduced the air quality in urban areas (Sun et al., 2006). The government should encourage green travel, vigorously develop urban public transport instead of private vehicles, thereby reducing urban particulate matter concentrations (Basagaña et al., 2018). In the urban areas, household electricity consumption is also a main contributor of particulate matter pollution (Yang et al., 2018). With the increase of household income and the rise in global temperature (Chuang et al., 2017), the frequency of household appliances use will also increase, which lead to more household electricity consumption. The increase of electricity consumption will increase the intensity of power generation, thereby emitting more pollutants due to thermal power generation. This means that household electricity consumption
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Fig. 3. PM10–2.5 pollution level of different prefecture-level cities in 2015 and 2016.
has significantly related to PM10–2.5 pollution (Bano et al., 2018; You et al., 2017). Particulate matter pollution can be generated at production (Zhang et al., 2017), transportation (Zhao et al., 2018b), and consumption stages (Shi et al., 2017) of consumer goods. The exhaust from heating also produces large amounts of air pollutants in the urban areas (Schiavon et al., 2015). At the national scale, comparing with other residents' activities, the contribution of urban heated area to PM10–2.5
pollution can be neglected, as heating only occurs in part of the prefecture-level cities. 4.5. Policy recommendations to control urban PM10–2.5 pollution The current ambient air quality regulations in Chinese cities are inclined to effectively control PM2.5 pollution and reduce the harm of PM2.5 pollution to human health. The prevention and control policy of
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Urban builtup area
Resident population 0.97
Total retail sales of consumer goods
0.87
Household electricity consumption
Civilian vehicles 0.82
0.99
0.94
0.11
Industrial Scale 0.99 Secondary industry GDP
0.48
Power generation
0.29
0.98 Residents’ Activities
0.99
City Size
Urban heated area
PM10-2.5 Pollution
0.17 0.99
0.88 Electricity consumption for industrial
Annual mean concentration of PM10-2.5
0.96
0.99
Heavy PM10-2.5 pollution days
90 percentile concentrations of PM10-2.5
Fig. 4. Results of fitted model. The numbers in the figure represent path coefficients, that is, the degree of contribution between variables.
PM10–2.5 pollution is still blank. From a public health point of view, Pascal et al. (2014) had confirmed that PM10–2.5 posed no less threat to human health than PM2.5. Based on the experiences, standards, methods, policies and other measures to control PM2.5 pollution, the government should combine the seasonal difference, spatial distribution and pollution sources of PM10–2.5 pollution and improve the relevant prevention and control measures of PM10–2.5 pollution in Chinese cities. Based on our research results, eleven targeted recommendations are proposed: 1) formulate the laws, regulations and pollution charge fee system for PM10–2.5 pollution as soon as possible; 2) optimize the industrial structure and develop the tertiary industry. In 2016, the Chinese government proposed ‘Supply-side Structural Reform’, one of the aims was to adjust the industrial structure and increased the proportion of the tertiary industry; 3) reduce unnecessary energy waste and replace thermal power with nuclear and hydroelectric power. Ding et al. (2018) had preliminarily compiled with water footprint inventories, which could provide theoretical support for water management strategies in China; 4) supervise enterprises to strictly implement end-of-pipe
Table 2 Effects of driving forces on PM10–2.5 pollution in China in 2016. The numbers in the table represent the normalized path coefficients, that is, the contribution of variables to PM10–2.5 pollution. Latent variables
Observed variables
Industrial scale Secondary industry GDP Power generation Electricity consumption for industrial City size Urban built-up area Resident population Residents' activities
Civilian vehicles Urban heated area Household electricity consumption Total retail sales of consumer goods
Direct Indirect Total influence influence influence 0.35 0.15 0.07 0.13
0.20 0.08 0.04 0.07
0.55 0.23 0.11 0.21
0 0 0 0.23 0.06 0.02 0.07 0.07
0.22 0.12 0.10 0 0 0 0 0
0.22 0.12 0.10 0.23 0.06 0.02 0.07 0.07
treatment. In recent years, Chinese scholars have attached great importance to emission monitoring of high-pollution industries (Liu et al., 2018b; Tian et al., 2014). Based on the cloud computing platform, Tsinghua University has developed the Multi-resolution Emission Inventory for China (MEIC, http://www.meicmodel.org/) to provide accurate data for scientific research, policy assessment and air quality management; 5) strengthen environmental protection awareness of residents, such as green travel and reasonable consumption. Since 2017, China had implemented the latest exhaust emission standards for the vehicles, which were the strictest environmental protection standards in the world; 6) install high-efficiency purifying machines in families and catering service institutions. At present, environmental protection departments of many cities require catering service institutions to install high-efficiency purifying machines to reduce urban air pollution; 7) use a new type of composite nanofiber membranes as air filter to reduce PM10–2.5 contaminants (Gao et al., 2017); 8) increase the relative humidity to scavenge particulate matter from the air through both physical and chemical processes (Cheng et al., 2015; Liu et al., 2016). Currently, China has established national-level urban wetland parks in 37 cities. The establishment of these wetland parks will play a positive role in the air quality of cities; 9) strengthen the joint control of urban agglomeration. Wang and Fang (2016) found that the spatial distribution of the particulate matter pollution of Bohai Rim Urban Agglomeration changed greatly with the seasons. The research results revealed that the formulation of urban air pollution prevention and control policies in the future should be considered from the whole urban agglomeration scale; 10) reduce straw burning in urban fringe areas and reduce the emission of air pollutants from straw burning (Wei et al., 2014). Considering potential agricultural and environmental benefits, China has begun to use straw to produce biochar-based fertilizers; 11) plan cities according to meteorological parameters (Amil et al., 2016; Buccolieri et al., 2011; Hsu and Cheng, 2016). China's urban areas are developing too fast and lacking reasonable planning, and local meteorological parameters should be considered for a better air pollution diffusion. 5. Conclusion Although high concentrations of PM10–2.5 pollution were often found in the urban areas in China, published studies on generation mechanism
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were still limited, making region-specific mechanism difficult to interpret. In this study, we first conducted a comparative analysis of the temporal dynamics of urban PM10–2.5 pollution in 2015 and 2016. The results showed that urban PM10–2.5 pollution was concentrated in central regions of China. Then, we quantified the effect of different forces driving urban PM10–2.5 pollution, and the results indicated that industrial scale contributed most to PM10–2.5 pollution among all the three latent variables. Among nine observed variables, secondary industry GDP were found to have the highest impact on PM10–2.5 pollution. The SEM used in the research can help to effectively understand the generation mechanism of urban PM10–2.5 pollution. This mechanism could be quantified for more in-depth understanding and more integrated results, which makes our research provide more effective theoretical guidance for the formulation of PM10–2.5 Pollution Prevention Action Plan. Overall, the variables included in our model could explain 42% of variation in PM10–2.5 pollution. In future studies, more anthropogenic sources should be taken into account to explain generation mechanism of PM10–2.5 pollution in more detail. Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.01.256.
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Identifying the contributions of multiple driving forces to PM10–2.5 pollution in urban areas in China Shuang Zhao a, Shiliang Liu a,⁎, Xiaoyun Hou a, Robert Beazley b, Yongxiu Sun a a b
School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, China Department of Natural Resources, College of Agriculture and Life Sciences, Fernow Hall 302, Cornell University, Ithaca, NY 14853, USA
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• PM10–2.5 poses a serious threat to human beings from a public health point of view. • Structural equation model is used to determine the driving mechanisms. • Urban PM10–2.5 pollution was concentrated in central regions of China. • Industrial scale contributed most to PM10–2.5 pollution in urban areas. • Eleven recommendations to control PM10–2.5 pollution are proposed.
a r t i c l e
i n f o
Article history: Received 11 October 2018 Received in revised form 23 December 2018 Accepted 18 January 2019 Available online 23 January 2019 Editor: Pavlos Kassomenos Keywords: PM10–2.5 pollution Mechanism analysis Anthropogenic source Driving force National scale
a b s t r a c t Economic development and urban expansion have accelerated particulate matter pollution in urban areas in China. Particulate matter-driven haze poses a serious threat to human beings from a public health point of view. Substantial evidences had linked adverse health effects with exposures to PM2.5, but recent research indicated that PM10–2.5 also had great risk. However, the relative contributions of driving forces to PM10–2.5 pollution are not well understood in the urban areas in China, and no targeted policies have been regulated to control the pollution. In this study, we quantified the contributions of potential driving factor across China with the structural equation model (SEM). Our results showed that in 2015 and 2016, the annual average concentrations of PM10–2.5 in the 290 prefecture-level cities with a mean value of 36 and 35 μg/m3, respectively. Industrial scale contributed more to PM10–2.5 pollution than city size and residents' activities in urban areas based on SEM results. Driving forces included in our model could explain 42% of variations in PM10–2.5 pollution, which indicated that there existed influences from other anthropogenic sources and natural sources. Eleven targeted recommendations were then proposed to control PM10–2.5 pollution based on our mechanism analysis. Findings from our study are beneficial to control PM10–2.5 pollution on a national scale, and also can provide a theoretical basis for the formulation of PM10–2.5 pollution control policy in China. © 2019 Elsevier B.V. All rights reserved.
1. Introduction
⁎ Corresponding author. E-mail address: [email protected] (S. Liu).
https://doi.org/10.1016/j.scitotenv.2019.01.256 0048-9697/© 2019 Elsevier B.V. All rights reserved.
Traffic, industrial activities, biomass burning, and rapid population growth in global urban areas all accelerate particulate matter pollution, which rapidly reduce air quality of the cities (Chirizzi et al., 2017;
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Karagulian et al., 2015). Particulate matter-driven haze became a common air pollution phenomenon in many urban areas of China (Liu et al., 2018a). The concentration of particulate matter far exceeds the national air quality standard of China (Wang et al., 2014). Numerous studies verified the adverse effects of short-term exposures to fine particulate matter (PM2.5; ≤2.5 μm in aerodynamic diameter) on human health (Pascal et al., 2014), while the adverse effects of coarse particulate matter (PM10–2.5; 2.5–10 μm in aerodynamic diameter) had been often ignored (Kassomenos et al., 2012). Recent research indicated that long-term or short-term exposures to PM10–2.5 was seriously hazardous to human life span (Ho et al., 2018; Pascal et al., 2014; Wu et al., 2018), artery (Adar et al., 2015; Zhang et al., 2014), pregnant women (Enders et al., 2019), infant survival (Son et al., 2011), infant birth weight (Ebisu et al., 2016), elderly (Han et al., 2017; Hassanvand et al., 2017), lower socio economic status (Ouidir et al., 2017), blood pressure (Honda et al., 2017), cerebrovascular (Wang et al., 2018) and reproductive health (Zhou et al., 2018). Although some aforementioned studies confirmed the harm of PM10–2.5, China had not set health-based standards for PM10–2.5. In 2016, the Chinese government gave up the ‘one-child’ policy, which had been implemented for N30 years. Since then, a large number of pregnant women and infants increased. Since PM10–2.5 is equally harmful to living organisms as PM2.5, we use the quality standard of PM2.5 to evaluate the pollution level of PM10–2.5 in our research. The annual average concentrations of PM10–2.5 in cities reached 36 μg/m3 and 35 μg/m3 in 2015 and 2016, respectively. These values are all overpassed the national air quality standard of China for PM2.5 (annual average concentrations b35 μg/m3). PM10–2.5 pollution has become an urgent air problem in China (Rönkkö et al., 2018). PM10–2.5 pollution is produced by a range of natural (such as windblown dust) and anthropogenic processes (such as vehicle emission, industrial and construction) in urban areas (Kassomenos et al., 2012). Compared with PM2.5, less is known about the driving forces of PM10–2.5 (Clements et al., 2016). Anthropogenic processes such as vehicle emissions, industrial and construction are considered to be the main source of PM10–2.5 pollution in China (Kassomenos et al., 2012; Lee et al., 2017). Some researchers had pointed out that industrial processing was the major source (Bano et al., 2018; Clements et al., 2014; Sawvel et al., 2015). Secondary inorganic aerosol emissions containing PM10–2.5 which were largely ascribed to vehicles (Bigi and Ghermandi, 2016; Clements et al., 2014) and ship (Bencs et al., 2017). Dust produced by construction, quarries, unpaved roads, and soil erosion made contributions to PM10–2.5 pollution in the urban areas (Li et al., 2013; Raysoni et al., 2016). Domestic cooking also promoted PM10–2.5 pollution (Bano et al., 2018). To determine the contributions of different factors, Landis et al. (2017) quantitatively analyzed PM10–2.5 sources in Athabasca Oil Sand Region using positive matrix factorization method. The results showed that fugitive haul road dust, fugitive oil sand, a mixed source fugitive dust, biomass combustion, mobile source, and a local copper factor were the top six main sources. Shahid et al. (2016) had similar conclusions that calcareous dust, siliceous dust, combustion particles and secondary organics had an 84% contribution to PM10–2.5 at an urban site in Pakistan. However, although higher concentrations of PM10–2.5 pollution were often found in urban areas in China, published literatures on generation mechanism were still limited, making region-specific mechanism difficult to interpret. The previous research lacked quantitative analysis about the multiple socioeconomic driving forces for PM10–2.5 pollution. The structural equation model (SEM) is a useful statistical approach to determine the theoretical causal relationships between driving forces and PM10–2.5 pollution (Beaumelle et al., 2016). In this study, we selected 290 prefecture-level cities with complete data in 2016 (including PM10–2.5 pollutant monitoring data and socioeconomic data) to examine the influence mechanisms of multiple forces driving urban PM10–2.5 pollution at national scale by constructing a reasonable SEM. The objectives of this research were to 1) quantify the
relative contribution of individual forces driving urban PM10–2.5 pollution; and 2) make policy recommendations for controlling PM10–2.5 pollution. The results of this study will provide theoretical advices for the formulation of urban PM10–2.5 control policies in the future. 2. Materials and methods 2.1. PM10–2.5 data collection After the optimization and adjustment of the National Air Monitoring Network, the monitoring sites expanded to the present 338 prefecture-level cities across China, and the number of ambient air quality monitoring points had been adjusted to 1497. All the monitoring data is uploaded to the National Urban Air Quality Real-time Publishing Platform of the China Environmental Monitoring Station (http://106.37. 208.233:20035/). The PM10–2.5 pollution data (2015 and 2016) we used in this study were downloaded from this platform (Appendix S1). After reviewing the integrity of the socioeconomic data, we finally selected 290 prefecture-level cities in 2016 across the country in which to analyze the impact of multiple socioeconomic factors on PM10–2.5 pollution (Fig. 1). 2.2. Socioeconomic data collection Two criteria were used to screen out the appropriate socioeconomic data. Firstly, the socioeconomic factors must have a direct or indirect impact on PM10–2.5 pollution, and these effects must be also documented by corresponding references. Secondly, there must be available data sources for the selected socioeconomic factors in all selected prefecture-level cities. Therefore, ten socioeconomic driving forces were selected according to the above criteria as follows: resident population, urban built-up area, secondary industry GDP, tertiary industry GDP, power generation, electricity consumption for industrial, household electricity consumption, total retail sales of consumer goods, civilian vehicles, and urban heated area (Table 1). Data for socioeconomic driving forces were collected from the China urban construction statistics yearbooks, China city statistical yearbook, corresponding literatures (Jiang et al., 2018), and official website data (Appendix S2). 2.3. Data analysis We established an initial SEM used the underlying relationships between different factors (Fig. 2). In the initial SEM, three latent variables were established, namely city size (determined by the observed variables of resident population and urban built-up area), industrial scale (determined by the observed variables of secondary industry GDP, tertiary industry GDP, power generation, and electricity consumption for industry), and residents' activities (determined by the observed variables of household electricity consumption, total retail sales of consumer goods, civilian vehicle, and urban heated area). Among these variables, industrial scale and residents' activities showed a direct driving forces on PM10–2.5 pollution, while city size was indirect. Akaike information criterion (AIC) and comparative fit index (CFI) were used to test the model. The optimal model should have the lowest AIC value and CFI value N0.90 (Bentler and Appelbaum, 1990). SEM analysis were performed in Amos 21.0 and R 3.4.2 (R Development Core Team, 2017). 3. Results 3.1. PM10–2.5 pollution level in urban China In 2015, the annual average concentrations of PM10–2.5 in the studied prefecture-level cities varied from 12 μg/m3 (Huangshan City) to 83 μg/ m3 (Jiayuguan City), with a mean value of 36 μg/m3 (Fig. 3, Appendix S1). The 90 percentile concentrations of PM10–2.5 varied from 20 μg/m3
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Fig. 1. The location of the selected prefecture-level cities in different provinces.
(Huangshan City) to 145 μg/m3 (Jiuquan City), with a mean value of 61 μg/m3. The number of heavy PM10–2.5 pollution days varied from 0 to 165 days (Hengshui City), with a mean value of 28 days. There were only 62 prefecture-level cities where heavy PM10–2.5 pollution had not occurred. In 2016, the annual average concentrations of PM10–2.5 in the studied prefecture-level cities varied from 10 μg/m3 (Heyuan City) to 83 μg/m3
(Jiuquan City), with a mean value of 35 μg/m3. The 90 percentile concentrations of PM10–2.5 varied from 18 μg/m3 (Nanping City) to 144 μg/m3 (Jiuquan City), with a mean value of 58 μg/m3. The number of heavy PM10–2.5 pollution days varied from 0 to 131 days (Jiuquan City), with a mean value of 26 days. There were 55 prefecture-level cities where heavy PM10–2.5 pollution had not occurred. It was evident that PM10–2.5 pollution in the two years were serious but varied insignificantly.
Table 1 Selected socioeconomic driving forces and their corresponding references.
3.2. The results of SEM
Driving forces
Reference sources
Industrial scale Secondary industry GDP Tertiary industry GDP Power generation Electricity consumption for industrial
Clements et al. (2014); Sawvel et al. (2015) Li et al. (2013); Raysoni et al. (2016) Landis et al. (2017); Shahid et al. (2016) Shahid et al. (2016)
City size Resident population Urban built-up area Residents' activities Household electricity consumption Total retail sales of consumer goods Civilian vehicles Urban heated area
Lamsal et al. (2013); Tian et al. (2013) Lyu et al. (2016); Raysoni et al. (2016)
Bano et al. (2018); Lamsal et al. (2013); You et al. (2017) Lyu et al. (2016) Bigi and Ghermandi (2016); Clements et al. (2014) Schiavon et al. (2015)
The influence of driving forces on PM10–2.5 pollution in SEM was shown in Fig. 4. The model had the lowest AIC value, and the CFI was 0.97 after the tertiary industry GDP factor was removed (Appendix S3). Industrial scale contributed to PM10–2.5 pollution more than other latent variables, and secondary industry GDP were found to have a higher impact on PM10–2.5 pollution than other observed variables, while urban heated area had the lowest (Table 2). Overall, the variables included in our model could explain 42% of variation in PM10–2.5 pollution. 4. Discussion 4.1. Spatial distribution of PM10–2.5 pollution in urban areas in China From the perspective of the whole China, PM10–2.5 pollution is concentrated in central regions, especially in Henan Province, Hebei Province, Shanxi Province, Shandong Province and Beijing city (Figs. 1, 2). These five areas had 330 million permanent residents in 2016, 4
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δ31
δ32
δ41
δ42
Urban builtup area
Resident population
Total retail sales of consumer goods
Civilian vehicles
δ43
δ44
Household electricity consumption
Urban heated area
City Size
Residents’ Activities
Industrial Scale
PM10-2.5 Pollution
Tertiary industry GDP
Secondary industry GDP
Power generation
Electricity consumption for industrial
Annual mean concentration of PM10-2.5
Heavy PM10-2.5 pollution days
90 percentile concentrations of PM10-2.5
δ21
δ22
δ23
δ24
δ11
δ12
δ13
Fig. 2. The initial frame for SEM. Blue represent driving forces that directly affect PM10–2.5 pollution, while green represent driving forces that indirect affect PM10–2.5 pollution. δ represent errors of the observed variables. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
national-level urban agglomerations, and a large number of industrial bases such as steel and coal mines, which all accelerated the production of PM10–2.5 pollution according to relevant literatures and our research results. The annual average concentrations of PM10–2.5 in cities of central China were mostly in the scope of the secondary standard (35–75 μg/ m3). The data used in this research were the annual average concentrations of PM10–2.5, which in some urban areas might mask the differences of PM10–2.5 concentrations between different months. For example, in Handan city of Hebei Province, the concentration of PM10–2.5 was 37 μg/m3 in the July, while it became 106 μg/m3 in the April and appeared to be serious PM10–2.5 pollution. The annual average PM10–2.5 concentrations in most southern cities were within the range of the first class standard (b35 μg/m3).
4.3. City size and PM10–2.5 pollution According to our research results, city size is an important indirect driver of PM10–2.5 pollution in China's urban areas, its contribution to PM10–2.5 pollution is through influencing residents' activities (Table 2). The increase of city size means more civilian vehicles, more construction activities and more electricity consumption, all of which contribute to PM10–2.5 pollution (Zhou et al., 2016). Lou et al. (2016) took urban areas as a ‘source’ landscape of particulate matter, and our results confirmed this statement. In addition, as the close connections of atmospheric circulation and regional atmospheric pollution occurs in northern China (Tao et al., 2014), PM10–2.5 can be long-range atmospheric transported (Kassomenos et al., 2012), joint prevention and control of PM10–2.5 pollution should be carried out among urban agglomerations.
4.2. Industrial scale and PM10–2.5 pollution In northern China, developed industrial activities such as steel and cement production remain the most important sources of urban particulate matter (Sawvel et al., 2015; Sanchez-Soberon et al., 2016). Significantly higher rates of lung cancer, respiratory and cardiovascular pathologies occurred due to industrial emissions of metal-rich particulate matter in the cities (Ruiz-Rudolph et al., 2016; Uzu et al., 2011). Reasonable adjustment of the proportions of secondary industry and tertiary industry, development of high-tech industry and transformation of the old industrial production system have become the key to reducing the level of particulate pollution in the cities. Thermal power generation produces large amounts of particle pollutants (RuizRudolph et al., 2016). The proportion of nuclear and hydroelectric power will increase due to less pollutant emissions. At present, the Chinese government has promulgated a series of policies to reduce the contribution of industrial activities to urban particulate pollution. For example, in order to reduce the concentration of particulate matter in the winter of the Beijing-Tianjin-Hebei region, ‘Action Plan for Comprehensive Treatment of Air Pollution in Autumn and Winter in Jing-Jin-Ji and Surrounding Areas’ have been implemented in heavy polluting industries such as steel, manufacturing and building materials in Hebei Province. In the foreseeable future, the contribution rate of industrial activities to urban air pollution will be significantly reduced due to the implementation of government targeted measures.
4.4. Residents' activities and PM10–2.5 pollution Residents' activities are another important direct driving forces of urban PM10–2.5 pollution. Civilian vehicles, household electricity consumption and total retail sales of consumer goods have similar contribution rates (Table 2). Civilian vehicles are considered to be the source of various air pollutants in the city (Zhao et al., 2018a), and residents near traffic network are affected by traffic-related PM10–2.5 pollution which can bring health risks (Oakes et al., 2016). With the increase of income, more vehicles are purchased and used by urban residents, and the wide use of low-quality gasoline have further reduced the air quality in urban areas (Sun et al., 2006). The government should encourage green travel, vigorously develop urban public transport instead of private vehicles, thereby reducing urban particulate matter concentrations (Basagaña et al., 2018). In the urban areas, household electricity consumption is also a main contributor of particulate matter pollution (Yang et al., 2018). With the increase of household income and the rise in global temperature (Chuang et al., 2017), the frequency of household appliances use will also increase, which lead to more household electricity consumption. The increase of electricity consumption will increase the intensity of power generation, thereby emitting more pollutants due to thermal power generation. This means that household electricity consumption
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Fig. 3. PM10–2.5 pollution level of different prefecture-level cities in 2015 and 2016.
has significantly related to PM10–2.5 pollution (Bano et al., 2018; You et al., 2017). Particulate matter pollution can be generated at production (Zhang et al., 2017), transportation (Zhao et al., 2018b), and consumption stages (Shi et al., 2017) of consumer goods. The exhaust from heating also produces large amounts of air pollutants in the urban areas (Schiavon et al., 2015). At the national scale, comparing with other residents' activities, the contribution of urban heated area to PM10–2.5
pollution can be neglected, as heating only occurs in part of the prefecture-level cities. 4.5. Policy recommendations to control urban PM10–2.5 pollution The current ambient air quality regulations in Chinese cities are inclined to effectively control PM2.5 pollution and reduce the harm of PM2.5 pollution to human health. The prevention and control policy of
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Urban builtup area
Resident population 0.97
Total retail sales of consumer goods
0.87
Household electricity consumption
Civilian vehicles 0.82
0.99
0.94
0.11
Industrial Scale 0.99 Secondary industry GDP
0.48
Power generation
0.29
0.98 Residents’ Activities
0.99
City Size
Urban heated area
PM10-2.5 Pollution
0.17 0.99
0.88 Electricity consumption for industrial
Annual mean concentration of PM10-2.5
0.96
0.99
Heavy PM10-2.5 pollution days
90 percentile concentrations of PM10-2.5
Fig. 4. Results of fitted model. The numbers in the figure represent path coefficients, that is, the degree of contribution between variables.
PM10–2.5 pollution is still blank. From a public health point of view, Pascal et al. (2014) had confirmed that PM10–2.5 posed no less threat to human health than PM2.5. Based on the experiences, standards, methods, policies and other measures to control PM2.5 pollution, the government should combine the seasonal difference, spatial distribution and pollution sources of PM10–2.5 pollution and improve the relevant prevention and control measures of PM10–2.5 pollution in Chinese cities. Based on our research results, eleven targeted recommendations are proposed: 1) formulate the laws, regulations and pollution charge fee system for PM10–2.5 pollution as soon as possible; 2) optimize the industrial structure and develop the tertiary industry. In 2016, the Chinese government proposed ‘Supply-side Structural Reform’, one of the aims was to adjust the industrial structure and increased the proportion of the tertiary industry; 3) reduce unnecessary energy waste and replace thermal power with nuclear and hydroelectric power. Ding et al. (2018) had preliminarily compiled with water footprint inventories, which could provide theoretical support for water management strategies in China; 4) supervise enterprises to strictly implement end-of-pipe
Table 2 Effects of driving forces on PM10–2.5 pollution in China in 2016. The numbers in the table represent the normalized path coefficients, that is, the contribution of variables to PM10–2.5 pollution. Latent variables
Observed variables
Industrial scale Secondary industry GDP Power generation Electricity consumption for industrial City size Urban built-up area Resident population Residents' activities
Civilian vehicles Urban heated area Household electricity consumption Total retail sales of consumer goods
Direct Indirect Total influence influence influence 0.35 0.15 0.07 0.13
0.20 0.08 0.04 0.07
0.55 0.23 0.11 0.21
0 0 0 0.23 0.06 0.02 0.07 0.07
0.22 0.12 0.10 0 0 0 0 0
0.22 0.12 0.10 0.23 0.06 0.02 0.07 0.07
treatment. In recent years, Chinese scholars have attached great importance to emission monitoring of high-pollution industries (Liu et al., 2018b; Tian et al., 2014). Based on the cloud computing platform, Tsinghua University has developed the Multi-resolution Emission Inventory for China (MEIC, http://www.meicmodel.org/) to provide accurate data for scientific research, policy assessment and air quality management; 5) strengthen environmental protection awareness of residents, such as green travel and reasonable consumption. Since 2017, China had implemented the latest exhaust emission standards for the vehicles, which were the strictest environmental protection standards in the world; 6) install high-efficiency purifying machines in families and catering service institutions. At present, environmental protection departments of many cities require catering service institutions to install high-efficiency purifying machines to reduce urban air pollution; 7) use a new type of composite nanofiber membranes as air filter to reduce PM10–2.5 contaminants (Gao et al., 2017); 8) increase the relative humidity to scavenge particulate matter from the air through both physical and chemical processes (Cheng et al., 2015; Liu et al., 2016). Currently, China has established national-level urban wetland parks in 37 cities. The establishment of these wetland parks will play a positive role in the air quality of cities; 9) strengthen the joint control of urban agglomeration. Wang and Fang (2016) found that the spatial distribution of the particulate matter pollution of Bohai Rim Urban Agglomeration changed greatly with the seasons. The research results revealed that the formulation of urban air pollution prevention and control policies in the future should be considered from the whole urban agglomeration scale; 10) reduce straw burning in urban fringe areas and reduce the emission of air pollutants from straw burning (Wei et al., 2014). Considering potential agricultural and environmental benefits, China has begun to use straw to produce biochar-based fertilizers; 11) plan cities according to meteorological parameters (Amil et al., 2016; Buccolieri et al., 2011; Hsu and Cheng, 2016). China's urban areas are developing too fast and lacking reasonable planning, and local meteorological parameters should be considered for a better air pollution diffusion. 5. Conclusion Although high concentrations of PM10–2.5 pollution were often found in the urban areas in China, published studies on generation mechanism
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were still limited, making region-specific mechanism difficult to interpret. In this study, we first conducted a comparative analysis of the temporal dynamics of urban PM10–2.5 pollution in 2015 and 2016. The results showed that urban PM10–2.5 pollution was concentrated in central regions of China. Then, we quantified the effect of different forces driving urban PM10–2.5 pollution, and the results indicated that industrial scale contributed most to PM10–2.5 pollution among all the three latent variables. Among nine observed variables, secondary industry GDP were found to have the highest impact on PM10–2.5 pollution. The SEM used in the research can help to effectively understand the generation mechanism of urban PM10–2.5 pollution. This mechanism could be quantified for more in-depth understanding and more integrated results, which makes our research provide more effective theoretical guidance for the formulation of PM10–2.5 Pollution Prevention Action Plan. Overall, the variables included in our model could explain 42% of variation in PM10–2.5 pollution. In future studies, more anthropogenic sources should be taken into account to explain generation mechanism of PM10–2.5 pollution in more detail. Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.01.256.
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